Identifying heart-brain interactions during internally and externally operative attention using conditional entropy

Advanced and long-term meditation and the autonomic nervous system: A review and synthesis

Meditation has become prominent in both clinical and non-clinical applications for its effects on psychological and physical well-being. Long-term meditators, who have dedicated extensive time to their practice, present a unique opportunity to explore the effects of prolonged meditation training on the autonomic nervous system. Research has reported concomitant activation of both sympathetic (aroused) and parasympathetic (relaxed) branches of the autonomic nervous system during some forms of meditation, leading to the term 'relaxed alertness.' However, findings are not consistent, with reports of both sympathetic and parasympathetic activation, sympathetic-only, parasympathetic-only, or temporally variable activations, depending on several factors. This review synthesizes these heterogeneous and seemingly inconsistent results in relation to three explanatory factors: (1) specific classification of style or type of meditation; (2) specific definition of the level of expertise of the meditators; and (3) intra-individual variations within a given meditation practice. When these factors are considered, convergent and meaningful patterns emerge, allowing for a shift from the broad notion of 'long-term' meditation to a more precise characterization of 'advanced' meditation, highlighting skills, states, and stages of mastery developed over time. Our synthesis is particularly useful for understanding both long-term and advanced meditation, as it reveals specific heart rate variability patterns, including very low and low-frequency spectral power peaks, along with cardiac and respiratory coupling. Better characterization of the role of the autonomic nervous system in the context of advanced meditation promises to inform improved meditation training, including training assisted by technology, toward more impactful outcomes.

Complexity synchronization analysis of neurophysiological data: Theory and methods

Introduction

We present a theoretical foundation based on the spontaneous self-organized temporal criticality (SOTC) and multifractal dimensionality μ to model complex neurophysiological and behavioral systems to infer the optimal empirical transfer of information among them. We hypothesize that heterogeneous time series characterizing the brain, heart, and lung organ-networks (ONs) are necessarily multifractal, whose level of complexity and, therefore, their information content is measured by their multifractal dimensions.

Methods

We apply modified diffusion entropy analysis (MDEA) to assess multifractal dimensions of ON time series (ONTS), and complexity synchronization (CS) analysis to infer information transfer among ONs that are part of a network-of-organ-networks (NoONs). An automated parameter selection process is proposed that relies on the Kolmogorov-Smirnov statistic to properly choose stripe sizes which are crucial in the MDEA analysis using synthetic duration times derived from the Mittag-Leffler map, shows the strength of KS-based stripe size selection to track changes in the IPL parameter μ . The purpose of this paper is to advance the validation, standardization, and reconstruct-ability of MDEA and CS analysis of heterogeneous neurophysiological time series data.

Results

Results from processing these datasets show that the complexity of brain, heart, and lung ONTS co-vary over time during cognitive task performance in 44% of subjects, while complexity of brain-heart interactions significantly co-vary in 85% of subjects.

Discussion

We conclude that certain principles, guidelines, and strategies for the application of MDEA analysis need further consideration. We conclude with a summary of the MDEA's limitations and future research directions.

Dynamic fluctuations in ascending heart-to-brain communication under mental stress

Dynamical information exchange between central and autonomic nervous systems, as referred to functional brain-heart interplay, occurs during emotional and physical arousal. It is well documented that physical and mental stress lead to sympathetic activation. Nevertheless, the role of autonomic inputs in nervous system-wise communication under mental stress is yet unknown. In this study, we estimated the causal and bidirectional neural modulations between electroencephalogram (EEG) oscillations and peripheral sympathetic and parasympathetic activities using a recently proposed computational framework for a functional brain-heart interplay assessment, namely the sympathovagal synthetic data generation model. Mental stress was elicited in 37 healthy volunteers by increasing their cognitive demands throughout three tasks associated with increased stress levels. Stress elicitation induced an increased variability in sympathovagal markers, as well as increased variability in the directional brain-heart interplay. The observed heart-to-brain interplay was primarily from sympathetic activity targeting a wide range of EEG oscillations, whereas variability in the efferent direction seemed mainly related to EEG oscillations in the γ band. These findings extend current knowledge on stress physiology, which mainly referred to top-down neural dynamics. Our results suggest that mental stress may not cause an increase in sympathetic activity exclusively as it initiates a dynamic fluctuation within brain-body networks including bidirectional interactions at a brain-heart level. We conclude that directional brain-heart interplay measurements may provide suitable biomarkers for a quantitative stress assessment and bodily feedback may modulate the perceived stress caused by increased cognitive demand.

Modeling brain-heart interactions from Poincaré plot-derived measures of sympathetic-vagal activity

Recent studies suggest that the interaction between the brain and heart plays a key role in cognitive processes, and measuring these interactions is crucial for understanding the interaction between the central and autonomic nervous systems. However, studying this bidirectional interplay presents methodological challenges, and there is still much room for exploration. This paper presents a new computational method called the Poincaré Sympathetic-Vagal Synthetic Data Generation Model (PSV-SDG) for estimating brain-heart interactions. The PSV-SDG combines EEG and cardiac sympathetic-vagal dynamics to provide time-varying and bidirectional estimators of mutual interplay. The method is grounded in the Poincaré plot, a heart rate variability method to estimate sympathetic-vagal activity that can account for potential non-linearities. This algorithm offers a new approach and computational tool for functional assessment of the interplay between EEG and cardiac sympathetic-vagal activity. The method is implemented in MATLAB under an open-source license. • A new brain-heart interaction modeling approach is proposed. • The modeling is based on coupled synthetic data generators of EEG and heart rate series. • Sympathetic and vagal activities are gathered from Poincaré plot geometry.

Brain-heart interactions in the neurobiology of consciousness

Recent experimental evidence on patients with disorders of consciousness revealed that observing brain-heart interactions helps to detect residual consciousness, even in patients with absence of behavioral signs of consciousness. Those findings support hypotheses suggesting that visceral activity is involved in the neurobiology of consciousness, and sum to the existing evidence in healthy participants in which the neural responses to heartbeats reveal perceptual and self-consciousness. More evidence obtained through mathematical modeling of physiological dynamics revealed that emotion processing is prompted by an initial modulation from ascending vagal inputs to the brain, followed by sustained bidirectional brain-heart interactions. Those findings support long-lasting hypotheses on the causal role of bodily activity in emotions, feelings, and potentially consciousness. In this paper, the theoretical landscape on the potential role of heartbeats in cognition and consciousness is reviewed, as well as the experimental evidence supporting these hypotheses. I advocate for methodological developments on the estimation of brain-heart interactions to uncover the role of cardiac inputs in the origin, levels, and contents of consciousness. The ongoing evidence depicts interactions further than the cortical responses evoked by each heartbeat, suggesting the potential presence of non-linear, complex, and bidirectional communication between brain and heartbeat dynamics. Further developments on methodologies to analyze brain-heart interactions may contribute to a better understanding of the physiological dynamics involved in homeostatic-allostatic control, cognitive functions, and consciousness.